Significant shifts in hydro-climatic regimes are expected in many parts of the world during the 21st century, affecting the water cycle. Vulnerability, impact, and adaptation studies often use tailored modeling chains to assess the expected effects of climate change, but the robustness of these chains is rarely investigated. This highlights the need for more rigorous evaluation of modeling chains to ensure that they are reliable for informed decision-making processes. To address this gap, we propose a framework for evaluating the sensitivity of hydrological scenario production to the bias correction step. We apply the framework to the Senegal River Basin, using three bias correction methods (linear scale, empirical quantile mapping, and nested bias correction) and three procedures (climate-correction, hydrological-correction, and climate-hydrological-correction). Our results show that the choice of modeling chain has a significant impact on future hydro-climatic trajectories. In particular, the combination of climate-and-hydrological-correction procedures may be optimal when both climate biases and hydrological model errors are significant. Moreover, using multiple bias correction methods can strengthen the ensemble of future hydro-climatic conditions. These findings have implications for vulnerability-impact-adaptation studies and underscore the importance of rigorous modeling chain design and sensitivity analysis.

Over the past few decades, significant research efforts have been devoted to the development of tools and techniques to improve the operational effectiveness of multireservoir systems. One of those efforts focuses on the incorporation of relevant hydrologic information into reservoir operation models. This effort is particularly relevant in regions characterized by low‐frequency climate signals, where time series of river discharges exhibit regime‐like behavior. Failure to properly capture such regime‐like behavior yields suboptimal operating policies, especially in systems characterized by large storage capacity such as large multireservoir systems. Hidden Markov Modeling is a class of hydrological models that can accommodate both overdispersion and serial dependence in time series, two essential hydrological properties that must be captured when modeling a system where the climate is switching between different states (e.g., dry, normal, and wet). In terms of reservoir operation, Stochastic Dual Dynamic Programming (SDDP) is one of the few optimization techniques that can accommodate both system and hydrologic complexity, that is, a large number of reservoirs and diverse hydrologic information. However, current SDDP formulations are unable to capture the long‐term persistence of the streamflow process found in some regions. In this paper, we present an extension of the SDDP algorithm that can handle the long‐term persistence and provide reservoir operating policies that explicitly capture regime shifts. Using the Senegal River Basin as a case study, we illustrate the potential gain associated with reservoir operating policies tailored to climate states.

Several alternatives have been proposed to shift the paradigms of water management under uncertainty from predictive to decision-centric. An often-mentioned tool is the response surface mapping system performance with a large sample of future hydroclimatic conditions through a stress test. Dividing this exposure space between acceptable and unacceptable states requires a criterion of acceptable performance defined by a threshold. In practice, however, stakeholders and decision-makers may be confronted with ambiguous objectives for which the acceptability threshold is not clearly defined (crisp). To accommodate such situations, this paper integrates fuzzy thresholds to the response surface tool. Such integration is not straightforward when response surfaces also have their own irreducible uncertainty from the limited number of descriptors and the stochasticity of hydroclimatic conditions. Incorporating fuzzy thresholds, therefore, requires articulating categories of imperfect knowledge that are different in nature, i.e., the irreducible uncertainty of the response itself relative to the variables that describe change and the ambiguity of the acceptability threshold. We, thus, propose possibilistic surfaces to assess flood vulnerability with fuzzy acceptability thresholds. An adaptation of the logistic regression for fuzzy set theory combines the probability of an acceptable outcome and the ambiguity of the acceptability criterion within a single possibility measure. We use the flood-prone reservoir system of the Upper Saint François River basin in Canada as a case study to illustrate the proposed approach. Results show how a fuzzy threshold can be quantitatively integrated when generating a response surface and how ignoring it might lead to different decisions. This study suggests that further conceptual developments could link the reliance on acceptability thresholds in bottom-up assessment frameworks with the current uses of fuzzy set theory.

The water resource of the Blue Nile River basin (BNRB) has been under pressure due to growing demands from many users, and the climate change impact. Potential impact of climate change for the maximum, median and minimum projected changes in the simulated streamflow of BNRB by a hydrologic model, VIC, driven by Representative Concentration Pathways climate scenarios, RCP4.5 and RCP8.5, of 4 GCMs (global climate models) downscaled dynamically by a regional climate model, WRF (Weather Research Forecasting) using a one-domain framework that covers the entire NRB for 2041-2070 and 2071-2100. These projected changes in streamflow were used to assess its future water allocations using a stochastic Dual Dynamic Programming (SDDP) algorithm and a hydro-economic model to optimize hydropower production and irrigated agriculture. Overall, it seems the Grand Ethiopian Renaissance Dam (GERD) reservoir will likely not operate at full storage level because the streamflow of BNRB is assumed to be regulated by three upstream reservoirs. The outflow from the reservoir of GERD or BNRB’s annual flow at Khartoum is projected to increase under maximum, but is expected to decrease under minimum and median projected changes in streamflow for 2041-2070 and 2071-2100, respectively. Given the annual net benefit obtained from hydropower production and irrigated agriculture of the reservoir is projected to increase (decrease) under the maximum (median and minimum) projected changes in streamflow, the potential climate change impact should be considered in designing and developing the future water resources of BNRB.

Several alternatives have been proposed to shift the paradigms of water management under uncertainty from predictive to decision-centric. An often-mentioned tool is the stress-test response surface, mapping system performance to a large sample of future hydro-climatic conditions. Dividing this exposure space between acceptable and unacceptable states requires a criterion of acceptable performance defined by a threshold. In practice, however, stakeholders and decision-makers may be confronted with ambiguous objectives for which the the acceptability threshold is not clearly defined (crisp). To accommodate such situations, this paper integrates fuzzy thresholds to the response surface tool. Such integration is not straightforward when response surfaces also have their own irreducible uncertainty, from the limited number of descriptors and the stochasticity of hydro-climatic conditions. Incorporating fuzzy thresholds therefore requires articulating uncertainties that are different in nature: the irreducible uncertainty of the response itself relative to the variables that describe change, and the ambiguity of the acceptability threshold. We thus propose possibilistic surfaces to assess flood vulnerability with fuzzy acceptability thresholds. An adaptation of the logistic regression for fuzzy set theory combines the probability of acceptable outcome and the ambiguity of the acceptability criterion within a single possibility measure. We use the flood-prone reservoir system of the Upper Saint-François River Basin in Canada as a case study to illustrate the proposed approach. Results show how a fuzzy threshold can be quantitatively integrated when generating a response surface, and how ignoring it might lead to different decisions. This study suggests that further theoretical development should link the decision-making under deep uncertainty framework with the existing experience of fuzzy set theory, notably for hydro-climatic vulnerability analysis.